Agile monitoring dashboard for clinical research studies.

Background: Clinical trial success hinges on efficient participant recruitment and retention. However, slow accrual and attrition frequently hinder progress. To address these challenges, a novel dashboard tool with control charts has been developed to provide investigators on the multi-site study of Delirium and Neuropsychological Recovery among Emergency General Surgery Survivors (DANE study) with timely information to improve trial recruitment.

Mortality following noncardiac surgery assessed by the Saint Louis University Score (SLUScore) for hypotension: a retrospective observational cohort study.

Background: The Saint Louis University Score (SLUScore) was developed to quantify intraoperative blood pressure trajectories and their associated risk for adverse outcomes. This study examines the prevalence and severity of intraoperative hypotension described by the SLUScore and its relationship with 30-day mortality in surgical subtypes.

Methods: This retrospective analysis of perioperative data included surgical cases performed between January 1, 2010, and December 31, 2020.

Prognostic models for predicting insomnia treatment outcomes: A systematic review.

Objective: To identify and critically evaluate models predicting insomnia treatment response in adult populations.

Methods: Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST).

Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review.

Background: The timely detection of Alzheimer's disease and other related dementias (ADRD) is suboptimal. Digital data already stored in electronic health records (EHR) offer opportunities for enhancing the timely detection of ADRD by facilitating the development of passive digital markers (PDMs). We conducted a systematic evidence review to identify studies that describe the development, performance, and validity of EHR-based PDMs for ADRD.

Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature.

Background: Severity of illness scores-Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Sequential Organ Failure Assessment-are current risk stratification and mortality prediction tools used in intensive care units (ICUs) worldwide. Developers of artificial intelligence or machine learning (ML) models predictive of ICU mortality use the severity of illness scores as a reference point when reporting the performance of these computational constructs.

Objective: This study aimed to perform a literature review and meta-analysis of articles that compared binary classification ML models with the severity of illness scores that predict ICU mortality and determine which models have superior performance.

Implementation of a risk-stratified approach to prevent postoperative nausea and vomiting in an institution with high baseline rates of prophylaxis.

Background And Aims: Although a risk-adjusted approach to preventing postoperative nausea and vomiting (PONV) is generally recommended, the successful implementation of such practice without mandated protocols remains elusive. To date, such a strategy has never been adapted to curb high baseline rates of prophylaxis.

Material And Methods: We conducted an observational study on a cohort of patients undergoing elective surgery before and after the implementation of a quality improvement initiative including a risk-stratified approach to prevent PONV.